Social scientists typically devote considerable effort to mitigating measurement error during
data collection but then ignore the issue during data analysis. Although many statistical …

Although social scientists devote considerable effort to mitigating measurement error during
data collection, they often ignore the issue during data analysis. And although many …

Multiple imputation was first conceived as a tool that statistical agencies could use to handle
nonresponse in large-sample public use surveys. In the last two decades, the multiple …

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies
provides a comprehensive introduction to the multiple imputation approach to missing data …

Adequately addressing missing data is a pervasive issue in the social sciences. Failure to
correctly address missing data can lead to biased or inefficient estimation of parameters …

Epidemiologic studies are frequently susceptible to missing information. Omitting
observations with missing variables remains a common strategy in epidemiologic studies …

Missing data are nearly always a problem in research, and missing values represent a
serious threat to the validity of inferences drawn from findings. Increasingly, social science …

Missing values present challenges in the analysis of data across many areas of research.
Handling incomplete data incorrectly can lead to bias, over-confident intervals, and …

Multiple imputation (MI) is often presented as an improvement over listwise deletion (LWD)
for regression estimation in the presence of missing data. Against a common view, we …

Missing data are frequently encountered in practice. A broader class of missing data is
called incomplete data, which includes data with measurement error, multilevel data with …